Visual File Types

Designing valuable high quality figures or illustrations takes time and energy. We can best take advantage of this investment by creating products that we can easily adjust, build upon, or reuse.

In order to do this, we need to consider how they’re composed, so that we understand what we’re working with.
Image files are almost always stored in one of two basic formats, (1) raster or (2) vector.

Raster displays (also called bitmap) like a computer screen, are composed of a rectangular grid of pixels. We fill each pixel with color, mosaic them together, and put together an image.

• Common file types:
  • PNG
  • JPEG
  • GIF

Vector displays are stored as lines and fills denoted by 2D points, and so an image is not subdividied into individual cells like in rasters. This allows vector images to scale better (both up and down in size).

• Common file types:
  • SVG
  • PDF
  • EPS
  • AI

 

Package Prep

Start by loading a few common packages for visualization in R. If you don’t have these packages already installed, we can do that first.

install.packages("ggplot2")
install.packages("phytools")
install.packages("gridExtra")
install.packages("RColorBrewer")
install.packages("viridis")
install.packages("dplyr")

 

Load ’em if you got ’em

library(ggplot2)
library(phytools)
library(gridExtra)
library(RColorBrewer)
library(viridis)
library(dplyr)

 

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Color Palettes

There are a million and one color palettes available in R now. It seems like a new one pops up everyday.
Let’s have a look at some color schemes

display.brewer.all()

Roughly 4.5% of the world’s population (345 million people!) are colorblind to some extent. So maybe let’s consider colorblind friendly palettes:

display.brewer.all(colorblindFriendly = T)

For reference, the package scales has a useful function show_col() for looking at palettes.
This gives us the hex numbers (#……) which we can use to choose specific colors. Hex numbers can also come in handy if you want to identify a specific color later in Adobe Illustrator.

library(scales)
library(wesanderson)
library(colRoz)
show_col(wes_palette("Zissou1", 6, type="continuous"))

show_col(brewer.pal(6, "Paired"))

show_col(colRoz_pal("c.kingii", 6))

show_col(viridis(6))

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Using Color and Shape in Figures

Enough of that, let’s load and plot some data to quickly mention color and shape.

goannas <- readRDS("Goannas.RDS")
names(goannas); 

## [1] "phy"               "size.data"         "distribution.data"
## [4] "rase.data"         "geo.object"

sizes <- goannas$size.data
sizes <- mutate(sizes, 
                TailRatio = Tail/Body_Length)
head(sizes)

##            Name_in_Tree Body_Length   Group     Habitat Tail TailRatio
## 1    Varanus.acanthurus       236.0 Monitor  saxicolous  380  1.610169
## 2     Varanus.balagardi       280.0 Monitor    arboreal  439  1.567857
## 3       Varanus.baritji       239.6 Monitor  saxicolous  297  1.239566
## 4    Varanus.brevicauda       102.0 Monitor terrestrial  113  1.107843
## 5         Varanus.bushi       114.5 Monitor    arboreal  159  1.388646
## 6 Varanus.caudolineatus       115.0 Monitor    arboreal  135  1.173913

Do a preliminary plot of the data so we can see what we want to change.

ggplot(sizes, aes(x=log(Body_Length), y=TailRatio)) +
  geom_point()

I don’t want to get too caught up here, but there are a few quick things we want to keep in mind.

• the grey background is cluttered
• the points are too small to see
• the data really aren’t telling us much here

We can try again, by adjusting just a few simple commands

ggplot(sizes, aes(x=log(Body_Length), y=TailRatio)) +
  geom_point(size=5, alpha=0.75, aes(color=Habitat)) + theme_bw()

This is still horrifying to the eyes.
Let’s think about how color and shape can improve the plot.

YGBplot <- ggplot(sizes, aes(x=log(Body_Length), y=TailRatio)) +
  geom_point(size = 5, aes(colour = Habitat)) + 
  scale_color_brewer(palette = "YlGnBu") +
    theme_classic()

SPCplot <- ggplot(sizes, aes(x=log(Body_Length), y=TailRatio)) +
  geom_point(size = 5, aes(colour = Habitat)) + 
  scale_color_brewer(palette = "Spectral") +
    theme_classic()

REDplot <- ggplot(sizes, aes(x=log(Body_Length), y=TailRatio)) +
  geom_point(size = 5, aes(colour = Habitat, shape = Habitat)) + 
  scale_shape_manual(values = c(15:18)) +
  scale_color_brewer(palette = "RdYlBu") +
    theme_classic()

BLUplot <- ggplot(sizes, aes(x=log(Body_Length), y=TailRatio)) +
  geom_point(size = 5, aes(colour = Habitat, shape = Habitat)) +
  scale_shape_manual(values = c(15:18)) +
  scale_color_brewer(palette = "Blues") +
    theme_classic()

together <- grid.arrange(SPCplot, YGBplot, BLUplot, REDplot, nrow=2)

Save this for later, and we can come back to edit it.

ggsave(filename = "Color_Comparison.pdf",
       plot = together,
       useDingbats = F)

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